Current management solutions that are available for searching data storage facilities that store volumes of electronic content use search algorithms that leverage query term frequency in content and inverse document frequency. In the current systems, search results can be impacted by user content ratings associated with the results. Users commonly rate search results manually through an optional method of simple indicators.
However, when systems focus on human-provided ratings, the results can have several disadvantages. First, a human often provides a content rating with little or no context. For example, a user content rating typically does not reference what search terms resulted in the content or how a user content rating is weighted in comparison to other ratings. Second, user content ratings are completely subjective and may reflect bias. For instance, a user who has authored content or a solution being rated may rate such solution highly regardless of whether the solution is accurate or not.
The methods and systems described below involve data storage facility search technologies that reduce the inaccuracies that can result from human ratings, and which instead focus on machine learning methodologies to help improve future search results.